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PaperclipPerfector  7mo ago (text post)   26343 thread views

Culture War Roundup for the week of February 16, 2026

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More recently, people charted other biases and found that most models had clear biases in terms of race, gender, sexual orientation, and nation of origin that are broadly in line with an aggressively intersectional, progressive worldview. Do modern models similarly have environmentalism baked in?

LLMs are actually great at picking up subtle nuances in the training data which humans would call stereotypes.

For chatbots, the general goal for RLHF is something like "humble, helpful, harmless". Picking a face of the Shoggoth comes with baggage. If I imagine a friendly, qualified operator of some helpline, the stereotype says they are likely a young college-educated woman.

I am sure that if you used RLHF to select a Shoggoth-face which talked like 4chan, you would get very different politics along with it.

and as of this morning, the big providers get it correct like somebody flipped a switch, provided you use that exact phrase, and you ask it in English.

This seems utterly pathetic on the part of the AI companies.

I mean, there are things which are outrage bait which need to be fixed, if your LLM happily generates python code which prints a recipe for methamphetamine, insults racial minorities with the right prompt or the like, you want to CYA and claim that you fixed this as soon as you became aware of the issue, even if the fix is just filtering any queries which mention both meth and python.

This however is not outrage bait. Papering over the flaws of your model one by one is utterly pointless.

Suppose I have a colleague who has written a function, and I tell him that actually his function fails for x=-1/3. What he then does is to add the following:

double f(double x)
{
+if (x==-1/3.)
+   return 42; // corrected value

This might be an acceptable short-term stopgap if I had indicated that his function failing for x=-1./3 (and only that value) is a showstopper for me, but in general it is a terrible idea. If his code fails for every x==-1./prime, then his approach will not converge to correct code any time soon, instead, he is simply papering over the specific errors people have found, e.g. cheating to make his code look correct when he knows it is not.

Ideally, AI companies would indicate a minimum of trustworthiness given that they are in a position of power (and possibly going to summon the demon which wipes out mankind). If they are already willing to cheat with this stuff which nobody would care about, what does that promise for the more serious stuff? It is like watching your new chief of police using a length of wire to steal a can of soda from a vending machine.

--

With regard to the content of the mail, this is a trick question of a similar category as the question if the pool of the Titanic is full or not. A human who thinks about these will typically think in images rather than words. I think that the Titanic one is a bit more vicious because the behavior of fluids is rarely spelled out by humans, while 'I drove my car through the car wash' should appear in the training data, while seawater flooding the swimming pool of a sinking vessel might not have appeared explicitly at all.

Suppose I have a colleague who has written a function, and I tell him that actually his function fails for x=-1/3. What he then does is to add the following:

This is an old criticism, older than me and I'm old as all hell. And indeed, it would make sense without realizing that training always has a constraint (implicit or explicit) on the size of the function, or in the case of LLMs, the number of parameters.

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